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A Dependency Parser for Spontaneous Chinese Spoken Language

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Published:25 July 2018Publication History
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Abstract

Dependency analysis is vital for spoken language understanding in spoken dialogue systems. However, existing research has mainly focused on western spoken languages, Japanese, and so on. Little research has been done for spoken Chinese in terms of dependency parsing. Therefore, the new spoken corpus, D-ESCSC (Dependency-Expressive Speech Corpus of Standard Chinese) is built by adding new dependency relations special to spoken Chinese based on a written Chinese annotation scheme. Since spoken Chinese contains typical ill-grammatical phenomena, e.g., translocation, repetition, duplication, and omission, the new atom feature related to punctuation and three feature templates are proposed to improve a graph-based dependency parser. Experimental results on spoken Chinese corpus show that the atom feature and three templates really work and the new parser outperforms the baseline parser. To our best knowledge, it is the first work to report dependency parsing results of spoken Chinese.

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  1. A Dependency Parser for Spontaneous Chinese Spoken Language

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          cover image ACM Transactions on Asian and Low-Resource Language Information Processing
          ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 17, Issue 4
          December 2018
          193 pages
          ISSN:2375-4699
          EISSN:2375-4702
          DOI:10.1145/3229525
          Issue’s Table of Contents

          Copyright © 2018 ACM

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          Publication History

          • Published: 25 July 2018
          • Accepted: 1 March 2018
          • Revised: 1 January 2018
          • Received: 1 October 2016
          Published in tallip Volume 17, Issue 4

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